Company Overview:

Our client is a leading global analytics and technology services firm specializing in the banking and financial services sector. They provide data-driven solutions to help clients optimize their risk management strategies, enhance customer experience, and improve operational efficiency. With a significant presence in major financial hubs worldwide, they serve a diverse portfolio of clients, including top-tier banks and retail banking institutions.

Role Overview:

As a Sr Consultant, you will be responsible for developing and implementing advanced analytical solutions for our banking clients, specifically focusing on credit risk and risk modeling. You will collaborate closely with cross-functional teams, including data scientists, business analysts, and technology experts, to deliver impactful insights and recommendations. This role offers the opportunity to directly contribute to improving our clients' risk management practices and driving better business outcomes.

Key Responsibilities:

- Develop and implement statistical models for credit risk assessment, including probability of default (PD), loss given default (LGD), and exposure at default (EAD) models, to enhance the accuracy of risk predictions for banking clients.

- Conduct in-depth data analysis and feature engineering to identify key drivers of credit risk and improve the predictive power of risk models, ultimately supporting better decision-making for retail banking portfolios.

- Collaborate with business stakeholders to understand their needs and translate them into actionable analytical solutions, ensuring alignment between technical implementations and business objectives.

- Communicate complex analytical findings and recommendations to both technical and non-technical audiences through clear and concise presentations and reports, facilitating informed decision-making at all levels.

- Monitor and validate the performance of risk models, identifying areas for improvement and implementing necessary model enhancements to maintain accuracy and stability.

- Stay up-to-date with the latest trends and techniques in data analytics, machine learning, and risk modeling, and apply them to improve our client's solutions.

Required Skillset:

- Demonstrated ability to develop and implement statistical models using techniques such as regression, time series analysis, and machine learning.

- Proven expertise in data analysis, feature engineering, and data visualization using tools such as Python, R, or SAS.

- Strong understanding of credit risk management principles and practices within the banking industry, particularly in retail banking.

- Excellent communication and interpersonal skills, with the ability to effectively collaborate with cross-functional teams and present complex information to diverse audiences.

- Experience in working with large datasets and cloud-based computing environments.

- Bachelor's or Master's degree in a quantitative field such as Statistics, Mathematics, Economics, or Computer Science.